Call summary

ABSTRACT

A faster and more streamlined system for providing summary and analysis of large amounts of communication data is described. System and methods are disclosed that employ an ontology to automatically summarize communication data and present the summary to the user in a form that does not require the user to listen to the communication data. In one embodiment, the summary is presented as written snippets, or short fragments, of relevant communication data that capture the meaning of the data relating to a search performed by the user. Such snippets may be based on theme and meaning unit identification.

BACKGROUND

The present disclosure relates to the field of automated dataprocessing, and more specifically to the application of ontologyprogramming to process and summarize communication data.

Prior art systems and methods for understanding content of communicationdata, such as customer service calls, require a user to personallyreview the individual data elements. For example, prior art systems forprocessing and understanding call center data require a user to listento customer service call recordings in order to understand the contentof the call data. Though some systems are capable of directing a user tothe point where certain content may occur in the call, the user stillmust play and listen to at least an identified section of data todetermine its contents and relevance. Prior art systems do not offer agood way to understand what is happening in large amounts of auditorydata, such as call center data, without listening to it. Thus, reviewingcommunication data using prior art systems takes a lot of time and iscumbersome.

SUMMARY

The present inventors recognize that a faster and more streamlinedsystem is needed for providing summary and analysis of large amounts ofcommunication data. Accordingly, the inventors developed the presentsystem and method that employs an ontology to automatically summarizecommunication data and present the summary to the user in a form thatdoes not require the user to listen to the communication data. In oneembodiment, the summary is presented as written snippets, or shortfragments, of relevant communication data that capture the meaning ofthe data relating to a search performed by the user. Such snippets maybe based on theme and meaning unit identification.

In one aspect of the disclosure a method of summarizing communicationdata is described. The method comprises receiving communication data;identifying one or more relevant themes in the communication data;locating the relevant themes in the communication data; creatingsnippets of the communication data to include the located relevantthemes; and displaying the snippets.

An initial step can consist of receiving one or more terms from a user.The step of identifying one or more relevant themes can includeidentifying one or more themes that relate to the one or more termsreceived from the user. Identifying one or more relevant themes caninclude presenting a list of themes to a user and receiving a selectionfrom the user of one or more of the themes presented in the list. Thelist of themes can include all of the themes present in a dataset. Thelist of themes can include a predefined number of themes that appearmost commonly in a dataset. The list of themes can include themes thatappear in a dataset at least a predefined number of times. Thecommunication data can be a transcript of an interpersonal interaction.Furthermore, the communication data can be divided into a plurality ofmeaning units; and one or more meaning units can be selected thatinclude the located relevant themes to create the snippets. Finally, thesnippets can be arranged temporally to provide a summary of thecommunication data.

In another aspect of the disclosure, a communication system forsummarizing communication data is described, the system comprising aprocessing system comprising computer-executable instructions stored onmemory that can be executed by a processor in order to: receivecommunication data; identify one or more relevant themes in thecommunication data; locate the relevant themes in the communicationdata; create snippets of the communication data to include the locatedrelevant themes; and display the snippets. The system of claim 10further comprising an initial step of receiving one or more terms from auser, and wherein the step of identifying one or more relevant themesincludes identifying one or more themes that relate to the one or moreterms received from the user. The step of identifying one or morerelevant themes includes presenting a list of themes to a user andreceiving a selection from the user of one or more of the themespresented in the list. The list of themes includes all of the themespresent in a dataset. The list of themes includes a predefined number ofthemes that appear most commonly in a dataset. The list of themes caninclude themes that appear in a dataset at least a predefined number oftimes. The communication data can be a transcript of an interpersonalinteraction. The communication data can be divided into a plurality ofmeaning units; and one or more meaning units can be selected thatinclude the located relevant themes to create the snippets. The snippetscan be arranged temporally to provide a summary of the communicationdata.

In another aspect of the disclosure, a computer readable non-transitorystorage medium comprising computer-executable instructions is disclosed,that when executed by a processor of a computing device performs amethod, comprising: receiving communication data; identifying one ormore relevant themes in the communication data; locating the relevantthemes in the communication data; creating snippets of the communicationdata to include the located relevant themes; and displaying thesnippets.

BRIEF DESCRIPTION

FIG. 1 depicts an exemplary embodiment of the ontology process andprogramming disclosed herein.

FIG. 2 is a flow chart depicting one embodiment of a method ofsummarizing communication data.

FIG. 3 is an exemplary embodiment of a display showing summaries ofcommunication data.

FIG. 4 is a system diagram of a system for creating a communication datasummary.

DETAILED DESCRIPTION

An ontology is a formal representation of a set of concepts, and therelationships between those concepts in a defined domain. The ontologymodels the specific meanings of terms as they apply to that domain, andmay be devised to incorporate one or several different spoken and/orwritten languages. As a non-limiting example, the ontologies describedherein are with respect to customer service interactions. A particularontology may be defined for a specific domain, such as financialservices, consumer products, subscription services, or some otherservice interactions.

Communication data may exist in the form of an audio recording,streaming audio, a transcription of spoken content, or any writtencorrespondence or communication. In the context of customer serviceinteractions, for example, communication content may exist as variousforms of data, including but not limited to audio recording, streamingaudio, transcribed textual transcript, or documents containing writtencommunications, such as email, physical mail, internet chat, textmessages, etc. In one embodiment, the communication data may be atranscript between a customer service agent or interactive voiceresponse (IVR) recording with a customer/caller. While the presentdisclosure is exemplified herein by describing an embodiment involvingthe analysis of audio data, such as recorded audio transcripts, it is tobe understood that in alternative embodiments, oral or writtencommunications may be used or analyzed. An ontology can be developed andapplied across all types of communication data, for example, all typesof customer interactions (which may include interactions in multiplelanguages), to develop a holistic tool for processing and interpretingsuch data.

The disclosed solution uses machine learning-based methods to improvethe knowledge extraction process in a specific domain or businessenvironment. By formulizing a specific company's internal knowledge andterminology, the ontology programming accounts for linguistic meaning tosurface relevant and important content for analysis. For example, thedisclosed ontology programming adapts to the language used in a specificdomain, including linguistic patterns and properties, such as wordorder, relationships between terms, and syntactical variations. Based onthe self-training mechanism developed by the inventors, the ontologyprogramming automatically trains itself to understand the businessenvironment by processing and analyzing a defined corpus ofcommunication data.

The premise on which the ontology is built is that meaningful terms aredetected in the corpus and then classified according to specificsemantic concepts, or entities. First, the corpus, or dataset, issegmented into meaning units. Meaning units are sequences of words thatexpress an idea, such as may be the equivalent of sentences. An exampleof a meaning unit in a customer service context would be the customerstatement “I would like to buy a phone.”

Within the meaning units, terms are identified and extracted. Termextraction is a process that reviews all meaning units and extracts theterms that are meaningful in a corpus. A term is a short list of words(e.g. between 1 and 5 words) that has a precise meaning, or a meaningthat stands out in its context. For example, “credit card” and “youraccount number” could both be appropriate terms. Terms are tagged in anon-overlapping way, with longer terms being generally preferred overshorter ones. For example, the term “my phone number” is counted as oneterm, rather than two—i.e. “my phone” and “my phone number.”

Once the main terms are defined, direct relations or linkages can beformed between these terms and their associated entities. Then, therelations are grouped into themes, which are groups or abstracts thatcontain synonymous relations. Relations are detected in interactions andsurfaced during the system's self-training process. A theme isessentially a single concept defined by its associated relations, whichrepresent that same concept among multiple interactions in the corpus.Themes provide users with a compressed view of the characteristics ofinteractions throughout the corpus. Themes may be identified accordingto the exemplary methods described herein. Themes provide a basis foranalytic functions of the ontological software, such as thecommunication data summary module described herein.

The presently disclosed ontology solution incorporates four main stages.As seen in FIG. 1, the four main stages include training 1, ontologyadministration 2, ontology tagging 3, and ontology analytics 4. Thetraining step 1 involves internal machine learning in which the systemlearns the customer's specific domain and formulates an initial ontology110. The initial ontology 110 is then passed to the ontologyadministration step 2 wherein the user reviews the initial ontology 110and refines it to create a refined ontology 210. The refined ontology210 is then stored and passed to the tagging module 3. Tagging is acontinuous online process that uses the ontology to tag tracked items inincoming interactions, and stores the tagged interactions in apersistent repository. Finally, the tagged interactions are then used bythe analytics module 4 to analyze and extract business data based on anenhanced formulization of a company's internal knowledge andterminology.

An ontology, which generally refers to a collection of entities andtheir relations, is one way in which an automated interpretation of acustomer service interaction can be developed, organized, and presentedas disclosed herein. Generally, an ontology as disclosed herein includesterms which are individual words or short phrases that represent thebasic units or concepts that might come up in the customer serviceinteraction. Non-limiting examples of terms, as used herein, include“device”, “iPhone”, “iPhone four”, “invoice”, “I”, “she”, “bill”,“cancel”, “upgrade”, “activate”, “broken”, or “cell phone”, “customercare”, or “credit card.” However, these are not intended to be limitingin any manner and are merely exemplary of basic units or concepts thatmay be found in a customer service interaction. All words in the corpuscan only be associated with one term, and each term can only be countedonce.

Classes are broader concepts that encapsulate or classify a set ofterms. Classes describe semantic concepts to which classified terms arerelated. It is also to be understood that classes may also classify orencapsulate a set of subclasses in which the terms are classified.Non-limiting examples of classes, may be include “objects”, “actions”,“modifiers”, “documents”, “service”, “customers”, or “locations”.However, these are not intended to be limiting on the types of classes,particularly the types of classes that may appear in an ontologydirected to a specific or specialized domain.

The classes, subclasses, and terms are connected by a plurality ofrelations which are defined binary directed relationships between termsand classes/subclasses or subclasses to classes. In a non-limitingexample, the term “pay” is defined under the class “action” and the term“bill” is defined in the class “documents”. Still further binarydirected relationships can be defined between these class/term pairs.The action/pay pair is related to the document/bill pair in that thepayment action requires an underlying document, which may be a bill. Inanother non-limiting example, the term “broken” is defined in the class“problems” and the term “iPhone” is defined in the class “device”. Theproblem/broken pair can also have a directed relationship to the“devices” class in which the “iPhone” term is a specific example asrepresented by the devices/iPhone pair.

In general, developing an ontology includes defining relations within adataset. Relations are linkages or relationships between the definedterms in the corpus. For example, “cancel>account,” “speakwith>supervisor,” and “buy>new iPhone” are exemplary relations. Thesystem defines a concise number of strong, meaningful relationsaccording to certain pre-defined policies or rules. Those strongrelations are given a higher score, and thus are given preference overother, lower-scoring relations.

Then, based upon the established relations, the system identifies, orsurfaces, themes appearing within the dataset. Themes are groups orcategories of relations that are similar in meaning A theme represents aconcept and is defined by its associated relations. A theme encapsulatesthe same concept among several interactions. Themes allow users toeasily and efficiently understand the characteristics of interactionsthroughout the corpus. For example, the theme “got an email” mightcorrespond to several relations, including “got the email,” “gotconfirmation,” “received an email,” “received an email confirmation,”etc. Preferably, the themes are expanded to incorporate as many of theidentified terms and relations as possible. Since data sets may commonlybe derived from speech-to-text translation algorithms, and because thosealgorithms are imperfect and often make slight mistranscriptions, it isdesirable to use algorithms that can associate textually similar termstogether—e.g., manager and managers, Sunday and Monday.

In a call center data set, for example, one theme may represent aconcept expressed in several different calls. In that way, a theme canprovide a summary, or a compressed view, of the characteristics of theinteractions in a communications data set. Preferably, a relation isassigned to only a single theme. Additionally, preferably only relationsare tagged in the tagging phase 3 of a corpus. Themes are used in theanalytics phase 4, and act as building blocks employed by analyticsapplications or modules, such as the summary module 1300 describedherein.

Themes can be displayed by displaying all of the relations comprisingthat theme and providing statistics about the appearance of suchrelations and/or the terms therein. In order to display a theme, or tocreate useful user interfaces displaying and conveying information aboutthemes and about a group of themes in a dataset, each theme should begiven a unique identifier, or theme name. For example, information aboutthemes and relations in a communication data set can be displayed bydisplaying the terms therein and connecting them with lines.

The theme name is an identifier for the theme that may be used, forexample, in user interfaces as a shortcut for conveying informationabout the theme using only a short string of words and/or characters.The theme name can be established by any number of methods. For example,the theme name can be created based on the top relation or relations inthe dataset, or a particular subset of the dataset being analyzed.Determination of the top relations may be based on any number offactors, or a combination thereof. For example, the theme name may bedevised by concatenating the terms of the first relation—“speak” and“manager”. In another embodiment, the name may be longer and mayinclude, for example, the top three relations, such as “speak, talk,find→manager, supervisor, superior.”

Based on the themes, analytics modules 4 can create summaries ofcommunication data. FIG. 2 provides an overview of an exemplary summarymethod and module 1300 for processing communication data to developrelevant summaries thereof. Such summaries may be summaries of datarelevant to a particular term or set of terms, such as term(s) enteredby a user. In the exemplary embodiment of the method depicted in FIG. 2,a user enters one or more terms at step 23 regarding which the userwants to assess the relevant communication data. In one embodiment, theuser may enter a term or terms into a dialog box or field.Alternatively, the user may be presented with a list of themes presentin a particular identified dataset, and the user could select one ormore of the listed themes. The listed themes may include all of thethemes present in a dataset, or they may include the themes mostcommonly appearing in the dataset. For example, the list may includeonly those themes that appear in a communication dataset at least apredefined number of times. In another embodiment, the list may includea predefined number of themes that appear most commonly in a dataset. Instill other embodiments, the list of themes may be created based onother methods.

Next, at step 25 the summary module 1300 identifies one or more themesrelated to the term(s). In an exemplary embodiment, the user enters theterm “supervisor.” From this entered term, the module may identify thetheme “speak supervisor.” The “speak supervisor” then may exemplarilyinclude a set of relations such as “talk→supervisor,” “get→manager,”“contact→supervisor”, etc. Then at step 29, the module 1300 locatesthose identified themes in the communication data. From there, themodule 1300 creates snippets 50 at step 31 based on the located themes,wherein snippets include the relevant communication data surrounding thelocated themes. For example, the snippets 50 may be created to include acertain number of characters, words, lines, and/or sentences in thecommunication data before and after each located theme. In analternative embodiment, the snippets may be created based on the meaningunit or units surrounding the located theme. Thus, the snippet may bethe meaning unit that includes the located theme. In some embodiments,the snippet may further include one or more meaning units before and/orafter the meaning unit that includes the theme. In still otherembodiments, the snippets may be comprised of only the theme name(s),which may include one or more relations encompassed in the theme, and/orpresent in the relevant portion of the communication data. Finally, atstep 33 the module 1300 displays the snippets 50 to the user, such as inthe form of the user display depicted in FIG. 3.

In still other embodiments, the module may provide snippets 50 relatedto the most important or common theme or themes appearing in aparticular dataset, such as a particular call or set of calls. In suchan embodiment the module 1300 may determine which themes represent themain purpose, or most significant aspect, of the communication data setor subset. In a customer service call center environment, for example,the most important theme or themes in a call recording would representthe main purpose or reason for the call. The snippets would then becreated to include such important themes, and would thereby summarizethe reason or purpose of the call.

As described herein, the snippets 50 produced by the summary moduleprovide the user with an easy and accessible summary of the relevantportions of a communication dataset. Thereby, the user can understandthe relevant aspects of the communication dataset without having tospend significant time reviewing portions of the data. In the customerservice call center context, this means that the user can understand thecontent of relevant customer service call interactions without having tolisten to the actual recorded calls. The snippets 50 may be arrangedtemporally so as to provide a sequential summary of important featuresof a dataset, such as a customer service interaction or a series ofcustomer service interactions.

FIG. 3 is an exemplary embodiment of an output of a summary module,which is a display showing summaries of communication data related tothe themes “emotion” and “speak supervisor”. As demonstrated in FIG. 3,the display may include a listing of the theme or themes being analyzed.Other relevant information about the query and/or the dataset may beincluded, such as identification of the dataset that is being analyzed,the time of the query, the number of relevant identified themes and/oroccurrences of those themes, etc. The snippets 50, or at least a portionthereof, are displayed which convey to the user the meaning of therelevant portions of the communication data.

FIG. 4 is a system diagram of an exemplary embodiment of a system 1200for automated language model adaptation implementing a summary module1300. The system 1200 is generally a computing system that includes aprocessing system 1206, storage system 1204, software 1202,communication interface 1208 and a user interface 1210. The processingsystem 1206 loads and executes software 1202 from the storage system1204, including application module 1230. When executed by the computingsystem 1200, application module 1230 directs the processing system 1206to operate as described in herein in further detail, including executionof the summary module 1300.

Although the computing system 1200 as depicted in FIG. 4 includes onesoftware module in the present example, it should be understood that oneor more modules could provide the same operation. Similarly, whiledescription as provided herein refers to a computing system 1200 and aprocessing system 1206, it is to be recognized that implementations ofsuch systems can be performed using one or more processors, which may becommunicatively connected, and such implementations are considered to bewithin the scope of the description.

The processing system 1206 can comprise a microprocessor and othercircuitry that retrieves and executes software 1202 from storage system1204. Processing system 1206 can be implemented within a singleprocessing device but can also be distributed across multiple processingdevices or sub-systems that cooperate in executing program instructions.Examples of processing system 1206 include general purpose centralprocessing units, application specific processors, and logic devices, aswell as any other type of processing device, combinations of processingdevices, or variations thereof.

The storage system 1204 can comprise any storage media readable byprocessing system 1206, and capable of storing software 1202. Thestorage system 1204 can include volatile and non-volatile, removable andnon-removable media implemented in any method or technology for storageof information, such as computer readable instructions, data structures,program modules, or other data. Storage system 1204 can be implementedas a single storage device but may also be implemented across multiplestorage devices or sub-systems. Storage system 1204 can further includeadditional elements, such a controller, capable of communicating withthe processing system 1206.

Examples of storage media include random access memory, read onlymemory, magnetic discs, optical discs, flash memory, virtual memory, andnon-virtual memory, magnetic sets, magnetic tape, magnetic disc storageor other magnetic storage devices, or any other medium which can be usedto storage the desired information and that may be accessed by aninstruction execution system, as well as any combination or variationthereof, or any other type of storage medium. In some implementations,the storage media can be a non-transitory storage media. In someimplementations, at least a portion of the storage media may betransitory. It should be understood that in no case is the storage mediaa propagated signal.

User interface 1210 can include a mouse, a keyboard, a voice inputdevice, a touch input device for receiving a gesture from a user, amotion input device for detecting non-touch gestures and other motionsby a user, and other comparable input devices and associated processingelements capable of receiving user input from a user. Output devicessuch as a video display or graphical display can display an interfacefurther associated with embodiments of the system and method asdisclosed herein. Speakers, printers, haptic devices and other types ofoutput devices may also be included in the user interface 1210. Asdisclosed in detail herein, the user interface 1210 operates to outputthe created snippets 20.

As described in further detail herein, the computing system 1200receives communication data 10. The communication data 10 may be, forexample, an audio recording or a conversation, which may exemplarily bebetween two speakers, although the audio recording may be any of avariety of other audio records, including multiple speakers, a singlespeaker, or an automated or recorded auditory message. The audio filemay exemplarily be a .WAV file, but may also be other types of audiofiles, exemplarily in a pulse code modulated (PCM) format and an examplemay include linear pulse code modulated (LPCM) audio data. Furthermore,the audio data is exemplarily mono audio data; however, it is recognizedthat embodiments of the method as disclosed herein may also be used withstereo audio data. In still further embodiments, the communication data10 may be streaming audio data received in real time or near-real timeby the computing system 1200.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to make and use the invention. The patentable scope of the inventionis designed by the claims, and may include other examples that occur tothose skilled in the art. Such other examples are intended to be withinthe scope of the claims if they have structural elements and/or methodsteps that to not differ from the literal language of the claims, or ifthey include equivalent structural elements and/or method steps withinsubstantial differences from the literal languages of the claims.

The invention claimed is:
 1. A method of automatically developing anontology via a computer system based on communication data andsummarizing the communication data using the ontology, wherein theontology is a structural representation of language elements and therelationships between those language elements within the dataset storedin memory of the computer system, the method comprising: receiving, bythe computer system, the communication data, wherein the communicationdata comprises conversational communication data; developing, by thecomputer system, the ontology by: segmenting the communication data intomeaning units, each meaning unit is a sequence of words that express anidea; identifying terms within the meaning units in the communicationdata, wherein the terms are individual words or short phrases thatrepresent basic concepts in the communication data; defining relationsbetween the terms, wherein the relations are defined binary directedrelationships between terms; and grouping the relations into themes,wherein each relation is grouped into only a single theme; identifying,by the computer system, one or more relevant themes in the communicationdata from the themes in the ontology, wherein the relevant themesinclude a set of the relations in the ontology that compose the relevantthemes; locating, by the computer system, the relevant themes in thecommunication data; creating, by the computer system, snippets of thecommunication data to include the located relevant themes, wherein thesnippets include a certain number of characters, words, lines, sentencesand/or meaning units in the communication data before and/or after eachlocated theme; and displaying, by the computer system, the snippets. 2.The method of claim 1 further comprising receiving one or more termsfrom a user, and wherein the step of identifying one or more relevantthemes includes identifying one or more themes that relate to the one ormore terms received from the user.
 3. The method of claim 1, wherein thestep of identifying one or more relevant themes includes presenting alist of themes in the ontology to a user and receiving a selection fromthe user of one or more of the themes presented in the list.
 4. Themethod of claim 3, wherein the list of themes includes all of the themesin the ontology.
 5. The method of claim 3, wherein the list of themesincludes a predefined number of themes in the ontology that appear mostcommonly in a dataset.
 6. The method of claim 3, wherein the list ofthemes includes themes in the ontology that appear in a dataset at leasta predefined number of times.
 7. The method of claim 1, wherein theconversational communication data is one or more selected from the groupconsisting of: a transcript of an interpersonal interaction; an audiorecording; streaming audio; transcription of spoken content; writtencorrespondence or communication; email; physical mail; internet chat;and text message.
 8. The method of claim 1, wherein creating thesnippets includes selecting one or more meaning units that include thelocated relevant themes.
 9. The method of claim 1, wherein the snippetsare arranged temporally to provide a summary of the communication data.10. A communication system for automatically developing an ontologybased on communication data and summarizing the communication data usingthe ontology, wherein the ontology is a structural representation oflanguage elements and the relationships between those language elementswithin the dataset stored in memory of the computer system, the systemcomprising a processing system comprising computer-executableinstructions stored on memory that can be executed by a processor inorder to: receive communication data, wherein the communication datacomprises conversational communication data; develop the ontologyincluding to: segment the communication data into meaning units, eachmeaning unit is a sequence of words that express an idea; identify termswithin the meaning units in the communication data, wherein the termsare individual words or short phrases that represent basic concepts inthe communication data; define relations between the terms, wherein therelations include defined binary directed relationships between terms;and group the relations into themes, wherein each relation is groupedinto only a single theme; identify one or more relevant themes in thecommunication data from the themes in the ontology, wherein the relevantthemes include a set of the relations in the ontology that compose therelevant themes; locate the relevant themes in the communication data;create snippets of the communication data to include the locatedrelevant themes, wherein the snippets include a certain number ofcharacters, words, lines, sentences and/or meaning units in thecommunication data before and/or after each located theme; and displaythe snippets.
 11. The system of claim 10, wherein thecomputer-executable instructions can be further executed by a processorin order to receive one or more terms from a user, and wherein the stepto identify one or more relevant themes includes to identify one or morethemes that relate to the one or more terms received from the user. 12.The system of claim 10, wherein the step to identify one or morerelevant themes includes to present a list of themes in the ontology toa user and receive a selection from the user of one or more of thethemes presented in the list.
 13. The system of claim 12, wherein thelist of themes includes all of the themes in the ontology.
 14. Thesystem of claim 12, wherein the list of themes includes a predefinednumber of themes in the ontology that appear most commonly in a dataset.15. The system of claim 12, wherein the list of themes includes themesin the ontology that appear in a dataset at least a predefined number oftimes.
 16. The system of claim 10, wherein the conversationalcommunication data is one or more selected from the group consisting of:a transcript of an interpersonal interaction; an audio recording;streaming audio; transcription of spoken content; written correspondenceor communication; email; physical mail; internet chat; and text message.17. The system of claim 10, wherein to create the snippets includes toselect one or more meaning units that include the located relevantthemes.
 18. The system of claim 10, wherein the snippets are arrangedtemporally to provide a summary of the communication data.
 19. Acomputer readable non-transitory storage medium comprisingcomputer-executable instructions that when executed by a processor of acomputing device performs a method of automatically developing anontology via a computer system based on communication data andsummarizing the communication data using the ontology, wherein theontology is a structural representation of language elements and therelationships between those language elements within the dataset storedin memory of the computer system, the method comprising: receiving thecommunication data, wherein the communication data comprisesconversational communication data; developing, by the computer system,the ontology by: segmenting the communication data into meaning units,each meaning unit is a sequence of words that express an idea;identifying terms within the meaning units in the communication data,wherein the terms are individual words or short phrases that representbasic concepts in the communication data; defining relations between theterms, wherein the relations are defined binary directed relationshipsbetween terms; and grouping the relations into themes, wherein eachrelation is grouped into only a single theme; identifying one or morerelevant themes in the communication data from the themes in theontology, wherein the relevant themes include a set of the relations inthe ontology that compose the relevant themes; locating the relevantthemes in the communication data; creating snippets of the communicationdata to include the located relevant themes, wherein the snippetsinclude a certain number of characters, words, lines, sentences and/ormeaning units in the communication data before and/or after each locatedtheme; and displaying the snippets.